Как направить запросы urllib через сеть TOR? [дубликат]

Основываясь на предложении @LukeDeLuccia, я адаптировал это решение для гауссовой фильтрации 1D-тензоров. Для справки ниже приведен рабочий пример:

import tensorflow as tf
from tensorflow.contrib.layers import fully_connected
import numpy as np
import matplotlib.pyplot as plt

# Based on: https://stackoverflow.com/a/52012658/1510542
# Credits to @zephyrus, @LukeDeLuccia, and @xdurch0


def gaussian_kernel(size, mean, std):
    d = tf.distributions.Normal(tf.cast(mean, tf.float32), tf.cast(std, tf.float32))
    vals = d.prob(tf.range(start=-size, limit=size+1, dtype=tf.float32))
    kernel = vals[:, tf.newaxis, tf.newaxis]
    return kernel / tf.reduce_sum(kernel)


def gaussian_filter(input, sigma):
    size = int(4*sigma + 0.5)
    x = input[:, :, tf.newaxis]
    kernel = gaussian_kernel(size=size, mean=0.0, std=sigma)
    conv = tf.nn.conv1d(x, kernel, stride=1, padding="SAME")
    return conv


def run_trainer():

    tf.reset_default_graph()

    # Define size of data, batch sizes
    N_data = 1000

    noise = 0.2 * (np.random.rand(N_data) - 0.5)
    x = np.linspace(0, 2*np.pi, N_data)
    y = np.sin(x)
    y_noisy = (y + noise).reshape((1, -1))

    input = tf.placeholder(tf.float32, shape=[None, N_data])
    fc1 = fully_connected(inputs=input, num_outputs=500, activation_fn=tf.nn.relu)
    fc2 = fully_connected(inputs=fc1, num_outputs=250, activation_fn=tf.nn.relu)
    fc3 = fully_connected(inputs=fc2, num_outputs=500, activation_fn=tf.nn.relu)
    output = fully_connected(inputs=fc3, num_outputs=1000, activation_fn=tf.nn.tanh)

    smooth_input = gaussian_filter(input, sigma=1)
    smooth_output = gaussian_filter(output, sigma=1)
    loss = tf.reduce_mean(tf.square(smooth_input - smooth_output))

    optimiser = tf.train.AdamOptimizer(2e-5)
    train_step = tf.contrib.training.create_train_op(loss, optimiser)

    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True

    with tf.Session(config=config) as sess:
        sess.run(tf.global_variables_initializer())

        for i in range(int(1e4)):

            _, loss_train = sess.run(
                [train_step, loss],
                feed_dict={input: y_noisy}
            )

            if i % 100 == 0:
                print("step: %i \t loss: %.3e" % (i, loss_train))

        result, smooth_result = sess.run(
            [output, smooth_output],
            feed_dict={input: y_noisy}
        )

        plt.plot(x, y_noisy.flatten())
        plt.plot(x, result.flatten())
        plt.plot(x, smooth_result.flatten())
        plt.show()


if __name__ == "__main__":
    run_trainer()